Systems and methods for user interface adaptation for per-user metrics

ABSTRACT

A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/117,140, which was filed Aug. 30, 2018. The entire disclosure of saidapplication is incorporated herein by reference.

FIELD

The present disclosure relates to user interface adaptation and, moreparticularly, to determining a per-user metric to transform the userinterface.

BACKGROUND

Currently, entities, such as high-volume pharmacies, offer online drugmanagement programs. For example, a user who is a member of a pharmacycan create an account on a user device via a web portal to access thedrug management program. Each user may be able to access the sameinformation and may be presented with an identical user interface.Similarly, a support representative or an analyst working for thepharmacy may access user information that is not customized to the useror the user population.

The background description provided here is for the purpose of generallypresenting the context of the disclosure. Work of the presently namedinventors, to the extent it is described in this background section, aswell as aspects of the description that may not otherwise qualify asprior art at the time of filing, are neither expressly nor impliedlyadmitted as prior art against the present disclosure.

SUMMARY

A computer system for dynamic adaptation of a user interface accordingto data store mining is presented. The system includes a data storeconfigured to index event data of a plurality of events. Each event ofthe plurality of events corresponds to a physical object being suppliedto a user identified by an identifier on behalf of a first entity. Thedata store is configured to store descriptive data for each of aplurality of identifiers. The system also includes a data analyst deviceconfigured to render the user interface to a data analyst and totransmit a message that identifies a selected identifier of theplurality of identifiers. The user interface includes a first userinterface element and a second user interface element.

The system further includes a data processing circuit configured toidentify a first set of identifiers from the plurality of identifiersbased on commonality among the descriptive data stored by the data storeacross the first set of identifiers. The data processing circuit is alsoconfigured to train a machine learning model for the first set ofidentifiers based on event data stored by the data store for the firstset of identifiers from within a predetermined epoch. The machinelearning model is trained using parallel processing of records from thedata store. The parallel processing includes assigning analysis of theindexed event data of each of a subset of the first set of identifiersto respective processor threads for parallel execution on processinghardware.

The system includes an interface circuit configured to, in response toreceiving the message from the data analyst device, determine an outputof the selected identifier using the machine learning model from thedata processing circuit. The output of the selected identifierrepresents an amount of resources expected to be used by the selectedidentifier for a second epoch subsequent to the predetermined epoch. Theinterface circuit is further configured to determine an interface metricfor the selected identifier based on the determined output of theselected identifier and transmit the interface metric to the dataanalyst device. The data analyst device is configured to, in response tothe interface metric from the interface circuit, selectively perform atleast one of modification and removal of the second user interfaceelement.

In other features, the interface circuit is configured to determine theinterface metric for the selected identifier further based on aretention value and a population retention value. The retention valueindicates a likelihood of the selected identifier being associated withthe first entity for the second epoch. The population retention valueindicates a likelihood of a population of identifiers encompassing theselected identifier being associated with the first entity for thesecond epoch. In other features, an operator of the data analyst deviceis one of (i) a user identified by the selected identifier, (ii) ananalyst for the first entity, and (iii) a support representative of thefirst entity.

In other features, a high-volume pharmacy includes the computer system,and the first entity is the high-volume pharmacy. In other features, theamount of resources expected to be used by the selected identifierincludes at least one of (i) an expected number of calls received fromthe selected identifier, (ii) an expected number of drug orders mailedto the selected identifier; and (iii) an expected measure of drugsdispensed to the selected identifier. In other features, the interfacecircuit is configured to, in response to receiving the message from thedata analyst device, determine an intake of the selected identifierusing the machine learning model from the data processing circuit. Theintake of the selected identifier represents an amount of resourcesexpected to be received by the first entity from the selected identifierfor the second epoch. The interface circuit is further configured todetermine the interface metric for the selected identifier based on thedetermined output of the selected identifier and the determined intakeof the selected identifier in response to receiving the message from thedata analyst device.

In other features, the amount of resources expected to be received fromthe selected identifier is a difference, for each event of the selectedidentifier, between an amount received for the event and an amountexpended for the event. In other features, the data analyst deviceincludes a persona determination module configured to transmit themessage to the interface circuit, receive the interface metric of theselected identifier from the interface circuit, and identify acorresponding persona of the selected identifier based on the receivedinterface metric of the selected identifier. In other features, the dataanalyst device includes a user interface adaptation module configured totransform the user interface of the data analyst device according to thecorresponding persona. In other features, the corresponding persona isselected from a set of personas including at least one of: (i) a set ofuser personas, (ii) a set of analyst personas. (iii) a set of supportpersonas.

In other features, the selected identifier is a population ofidentifiers encompassing the selected identifier. In other features,descriptive data for each of the plurality of identifiers includes atleast one of: (i) name, (ii) support call logs, (iii) support chat logs;and (iv) selected identifier subscription duration. In other features,descriptive data for each of the plurality of identifiers includespopulation of identifiers encompassing the selected identifiersubscription duration. In other features, the event data includes atleast one of, for each event (i) a respective intake and (ii) arespective output. In other features, the interface circuit includes anactual retention module configured to identify a population ofidentifiers encompassing the selected identifier, determine a previousretention value of the selected identifier based on a duration ofpresence of the selected identifier within the identified populationduring the predetermined epoch, and determine a previous populationretention value of the identified population based on a duration ofpresence of the identified population as a client of the first entityduring the predetermined epoch.

In other features, the interface circuit calculates the interface metricaccording to an equation. The equation is:

${{PUM}_{k} = {A_{k}{\sum\limits_{i = 1}^{n}\; \frac{{G_{i}\left( {M_{k} - C_{k}} \right)}r^{i}}{\left( {1 + d} \right)^{i}}}}},$

where PUM_(k) is the interface metric of the selected identifier, A_(k)is a starting retention age of the selected identifier, n is a length ofthe second epoch in years, G_(i) is an estimate of a retention value ofthe selected identifier at year i of the second epoch, M_(k) is anestimated intake of the selected identifier for the second epoch, C_(k)is the determined output of the selected identifier, r is an annualpopulation retention value; and d is a predetermined discount rate. Inother features, the length of the second epoch in years is an integergreater than or equal to one, the estimate of the retention value isless than or equal to zero, the annual population retention value isless than or equal to one; and the predetermined discount rate isgreater than or equal to zero and less than one. In other features, thesecond user interface element indicates a shipping option of a drug. Inother features, the at least one of modification and removal of thesecond user interface element includes, based on the interface metric,updating the shipping option of the drug to an expedited shippingoption.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description, the claims, and the drawings.The detailed description and specific examples are intended for purposesof illustration only and are not intended to limit the scope of thedisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings.

FIG. 1 is a functional block diagram of an example system including ahigh-volume pharmacy.

FIG. 2 is a functional block diagram of an example pharmacy fulfillmentdevice, which may be deployed within the system of FIG. 1.

FIG. 3 is a functional block diagram of an example order processingdevice, which may be deployed within the system of FIG. 1.

FIG. 4 is a functional block diagram of an example per-user metricdetermination device.

FIG. 5 is a flowchart of an example per-user metric being determined.

FIG. 6 is a flowchart of an example per-user metric calculation.

FIG. 7 is a flowchart of an example persona selection for a web portal.

FIG. 8 is a flowchart of an example persona selection for a supportdevice.

FIG. 9A is an example user interface adaptation of a prescriptioninterface for a low per-user metric.

FIG. 9B is an example user interface adaptation of a prescriptioninterface for a high per-user metric.

In the drawings, reference numbers may be reused to identify similarand/or identical elements.

DETAILED DESCRIPTION Introduction

Adapting a user interface based on a per-user metric provides apersonalized user interface, including personalized options andbenefits, directly to a user. In various implementations, user interfaceadaptation based on the per-user metric can also be provided to supportrepresentatives and analysts when using their respective devices. Theper-user metric is determined based on data stored in a storage deviceof a pharmacy—for example, member data and claims data stored in thestorage device of the pharmacy.

In various implementations, the per-user metric can be determined basedon an expected user retention, an expected population retention, anexpected output of the user, and an expected intake of the user. Forexample, the expected user retention not only considers the expecteduser retention with the pharmacy, but also considers the expected userretention within the user population—for example, the user's employer.

Additionally, to determine the other features used to determine theper-user metric, a training group of available member claims and claimsdata can be analyzed to determined expected future values. For example,to anticipate the expected population retention, a likelihood ofretention is calculated based on historical retention rates. Similarly,the expected output and intake are calculated based on historicaloutputs and intakes.

Once determined, the per-user metric is used to customize the userinterface displayed to the user, an analyst, and/or a supportrepresentative. For example, based on the per-user metric, acorresponding persona is selected for display to the user at the userdevice. Based on the selected persona, the user interface is transformedto encourage that user's retention and to increase that user's per-usermetric. In various implementations, the expected user retention is anadditional factor considered when transforming the user interface.

For the analyst, the user interface may be transformed according to apersona corresponding to the analyzed user or user population's per-usermetric. Additionally, for analyst purposes, the per-user metric may bedisplayed along with analytics related to the user or user populationbeing analyzed. For the support representative, the user interface maytransform according to the user persona and provide the supportrepresentative with available options to offer the user.

High-Volume Pharmacy

FIG. 1 is a block diagram of an example implementation of a system 100for a high-volume pharmacy. While the system 100 is generally describedas being deployed in a high-volume pharmacy or a fulfillment center (forexample, a mail order pharmacy, a direct delivery pharmacy, etc.), thesystem 100 and/or components of the system 100 may otherwise be deployed(for example, in a lower-volume pharmacy, etc.). A high-volume pharmacymay be a pharmacy that is capable of filling at least some prescriptionsmechanically. The system 100 may include a benefit manager device 102and a pharmacy device 106 in communication with each other directlyand/or over a network 104. The system 100 may also include a storagedevice 110.

The benefit manager device 102 is a device operated by an entity that isat least partially responsible for creation and/or management of thepharmacy or drug benefit. While the entity operating the benefit managerdevice 102 is typically a pharmacy benefit manager (PBM), other entitiesmay operate the benefit manager device 102 on behalf of themselves orother entities (such as PBMs). For example, the benefit manager device102 may be operated by a health plan, a retail pharmacy chain, a drugwholesaler, a data analytics or other type of software-related company,etc. In some implementations, a PBM that provides the pharmacy benefitmay provide one or more additional benefits including a medical orhealth benefit, a dental benefit, a vision benefit, a wellness benefit,a radiology benefit, a pet care benefit, an insurance benefit, a longterm care benefit, a nursing home benefit, etc. The PBM may, in additionto its PBM operations, operate one or more pharmacies. The pharmaciesmay be retail pharmacies, mail order pharmacies, etc.

Some of the operations of the PBM that operates the benefit managerdevice 102 may include the following activities and processes. A member(or a person on behalf of the member) of a pharmacy benefit plan mayobtain a prescription drug at a retail pharmacy location (e.g., alocation of a physical store) from a pharmacist or a pharmacisttechnician. The member may also obtain the prescription drug throughmail order drug delivery from a mail order pharmacy location, such asthe system 100. In some implementations, the member may obtain theprescription drug directly or indirectly through the use of a machine,such as a kiosk, a vending unit, a mobile electronic device, or adifferent type of mechanical device, electrical device, electroniccommunication device, and/or computing device. Such a machine may befilled with the prescription drug in prescription packaging, which mayinclude multiple prescription components, by the system 100. Thepharmacy benefit plan is administered by or through the benefit managerdevice 102.

The member may have a copayment for the prescription drug that reflectsan amount of money that the member is responsible to pay the pharmacyfor the prescription drug. The money paid by the member to the pharmacymay come from, as examples, personal funds of the member, a healthsavings account (HSA) of the member or the member's family, a healthreimbursement arrangement (HRA) of the member or the member's family, ora flexible spending account (FSA) of the member or the member's family.In some instances, an employer of the member may directly or indirectlyfund or reimburse the member for the copayments.

The amount of the copayment required by the member may vary acrossdifferent pharmacy benefit plans having different plan sponsors orclients and/or for different prescription drugs. The member's copaymentmay be a flat copayment (in one example, $10), coinsurance (in oneexample, 10%), and/or a deductible (for example, responsibility for thefirst $500 of annual prescription drug expense, etc.) for certainprescription drugs, certain types and/or classes of prescription drugs,and/or all prescription drugs. The copayment may be stored in thestorage device 110 or determined by the benefit manager device 102.

In some instances, the member may not pay the copayment or may only paya portion of the copayment for the prescription drug. For example, if ausual and customary cost for a generic version of a prescription drug is$4, and the member's flat copayment is $20 for the prescription drug,the member may only need to pay $4 to receive the prescription drug. Inanother example involving a worker's compensation claim, no copaymentmay be due by the member for the prescription drug.

In addition, copayments may also vary based on different deliverychannels for the prescription drug. For example, the copayment forreceiving the prescription drug from a mail order pharmacy location maybe less than the copayment for receiving the prescription drug from aretail pharmacy location.

In conjunction with receiving a copayment (if any) from the member anddispensing the prescription drug to the member, the pharmacy submits aclaim to the PBM for the prescription drug. After receiving the claim,the PBM (such as by using the benefit manager device 102) may performcertain adjudication operations including verifying eligibility for themember, identifying/reviewing an applicable formulary for the member todetermine any appropriate copayment, coinsurance, and deductible for theprescription drug, and performing a drug utilization review (DUR) forthe member. Further, the PBM may provide a response to the pharmacy (forexample, the pharmacy system 100) following performance of at least someof the aforementioned operations.

As part of the adjudication, a plan sponsor (or the PBM on behalf of theplan sponsor) ultimately reimburses the pharmacy for filling theprescription drug when the prescription drug was successfullyadjudicated. The aforementioned adjudication operations generally occurbefore the copayment is received and the prescription drug is dispensed.However in some instances, these operations may occur simultaneously,substantially simultaneously, or in a different order. In addition, moreor fewer adjudication operations may be performed as at least part ofthe adjudication process.

The amount of reimbursement paid to the pharmacy by a plan sponsorand/or money paid by the member may be determined at least partiallybased on types of pharmacy networks in which the pharmacy is included.In some implementations, the amount may also be determined based onother factors. For example, if the member pays the pharmacy for theprescription drug without using the prescription or drug benefitprovided by the PBM, the amount of money paid by the member may behigher than when the member uses the prescription or drug benefit. Insome implementations, the amount of money received by the pharmacy fordispensing the prescription drug and for the prescription drug itselfmay be higher than when the member uses the prescription or drugbenefit. Some or all of the foregoing operations may be performed byexecuting instructions stored in the benefit manager device 102 and/oran additional device.

Examples of the network 104 include a Global System for MobileCommunications (GSM) network, a code division multiple access (CDMA)network, 3rd Generation Partnership Project (3GPP), an Internet Protocol(IP) network, a Wireless Application Protocol (WAP) network, or an IEEE802.11 standards network, as well as various combinations of the abovenetworks. The network 104 may include an optical network. The network104 may be a local area network or a global communication network, suchas the Internet. In some implementations, the network 104 may include anetwork dedicated to prescription orders: a prescribing network such asthe electronic prescribing network operated by Surescripts of Arlington,Va.

Moreover, although the system shows a single network 104, multiplenetworks can be used. The multiple networks may communicate in seriesand/or parallel with each other to link the devices 102-110.

The pharmacy device 106 may be a device associated with a retailpharmacy location (e.g., an exclusive pharmacy location, a grocery storewith a retail pharmacy, or a general sales store with a retail pharmacy)or other type of pharmacy location at which a member attempts to obtaina prescription. The pharmacy may use the pharmacy device 106 to submitthe claim to the PBM for adjudication.

Additionally, in some implementations, the pharmacy device 106 mayenable information exchange between the pharmacy and the PBM. Forexample, this may allow the sharing of member information such as drughistory that may allow the pharmacy to better service a member (forexample, by providing more informed therapy consultation and druginteraction information). In some implementations, the benefit managerdevice 102 may track prescription drug fulfillment and/or otherinformation for users that are not members, or have not identifiedthemselves as members, at the time (or in conjunction with the time) inwhich they seek to have a prescription filled at a pharmacy.

The pharmacy device 106 may include a pharmacy fulfillment device 112,an order processing device 114, and a pharmacy management device 116 incommunication with each other directly and/or over the network 104. Theorder processing device 114 may receive information regarding fillingprescriptions and may direct an order component to one or more devicesof the pharmacy fulfillment device 112 at a pharmacy. The pharmacyfulfillment device 112 may fulfill, dispense, aggregate, and/or pack theorder components of the prescription drugs in accordance with one ormore prescription orders directed by the order processing device 114.

In general, the order processing device 114 is a device located withinor otherwise associated with the pharmacy to enable the pharmacyfulfilment device 112 to fulfill a prescription and dispenseprescription drugs. In some implementations, the order processing device114 may be an external order processing device separate from thepharmacy and in communication with other devices located within thepharmacy.

For example, the external order processing device may communicate withan internal pharmacy order processing device and/or other deviceslocated within the system 100. In some implementations, the externalorder processing device may have limited functionality (e.g., asoperated by a user requesting fulfillment of a prescription drug), whilethe internal pharmacy order processing device may have greaterfunctionality (e.g., as operated by a pharmacist).

The order processing device 114 may track the prescription order as itis fulfilled by the pharmacy fulfillment device 112. The prescriptionorder may include one or more prescription drugs to be filled by thepharmacy. The order processing device 114 may make pharmacy routingdecisions and/or order consolidation decisions for the particularprescription order. The pharmacy routing decisions include whatdevice(s) in the pharmacy are responsible for filling or otherwisehandling certain portions of the prescription order. The orderconsolidation decisions include whether portions of one prescriptionorder or multiple prescription orders should be shipped together for auser or a user family. The order processing device 114 may also trackand/or schedule literature or paperwork associated with eachprescription order or multiple prescription orders that are beingshipped together. In some implementations, the order processing device114 may operate in combination with the pharmacy management device 116.

The order processing device 114 may include circuitry, a processor, amemory to store data and instructions, and communication functionality.The order processing device 114 is dedicated to performing processes,methods, and/or instructions described in this application. Other typesof electronic devices may also be used that are specifically configuredto implement the processes, methods, and/or instructions described infurther detail below.

In some implementations, at least some functionality of the orderprocessing device 114 may be included in the pharmacy management device116. The order processing device 114 may be in a client-serverrelationship with the pharmacy management device 116, in a peer-to-peerrelationship with the pharmacy management device 116, or in a differenttype of relationship with the pharmacy management device 116. The orderprocessing device 114 and/or the pharmacy management device 116 maycommunicate directly (for example, such as by using a local storage)and/or through the network 104 (such as by using a cloud storageconfiguration, software as a service, etc.) with the storage device 110.

The storage device 110 may include: non-transitory storage (for example,memory, hard disk, CD-ROM, etc.) in communication with the benefitmanager device 102 and/or the pharmacy device 106 directly and/or overthe network 104. The non-transitory storage may store order data 118,member data 120, claims data 122, drug data 124, prescription data 126,and/or plan sponsor data 128. Further, the system 100 may includeadditional devices, which may communicate with each other directly orover the network 104.

The order data 118 may be related to a prescription order. The orderdata may include type of the prescription drug (for example, drug nameand strength) and quantity of the prescription drug. The order data 118may also include data used for completion of the prescription, such asprescription materials. In general, prescription materials include anelectronic copy of information regarding the prescription drug forinclusion with or otherwise in conjunction with the fulfilledprescription. The prescription materials may include electronicinformation regarding drug interaction warnings, recommended usage,possible side effects, expiration date, date of prescribing, etc. Theorder data 118 may be used by a high-volume fulfillment center tofulfill a pharmacy order.

In some implementations, the order data 118 includes verificationinformation associated with fulfillment of the prescription in thepharmacy. For example, the order data 118 may include videos and/orimages taken of (i) the prescription drug prior to dispensing, duringdispensing, and/or after dispensing, (ii) the prescription container(for example, a prescription container and sealing lid, prescriptionpackaging, etc.) used to contain the prescription drug prior todispensing, during dispensing, and/or after dispensing, (iii) thepackaging and/or packaging materials used to ship or otherwise deliverthe prescription drug prior to dispensing, during dispensing, and/orafter dispensing, and/or (iv) the fulfillment process within thepharmacy. Other types of verification information such as barcode dataread from pallets, bins, trays, or carts used to transport prescriptionswithin the pharmacy may also be stored as order data 118.

The member data 120 includes information regarding the membersassociated with the PBM. The information stored as member data 120 mayinclude personal information, personal health information, protectedhealth information, etc. Examples of the member data 120 include name,address, telephone number, e-mail address, prescription drug history,etc. The member data 120 may include a plan sponsor identifier thatidentifies the plan sponsor associated with the member and/or a memberidentifier that identifies the member to the plan sponsor. The memberdata 120 may include a member identifier that identifies the plansponsor associated with the user and/or a user identifier thatidentifies the user to the plan sponsor. The member data 120 may alsoinclude dispensation preferences such as type of label, type of cap,message preferences, language preferences, etc.

The member data 120 may be accessed by various devices in the pharmacy(for example, the high-volume fulfillment center, etc.) to obtaininformation used for fulfillment and shipping of prescription orders. Insome implementations, an external order processing device operated by oron behalf of a member may have access to at least a portion of themember data 120 for review, verification, or other purposes.

In some implementations, the member data 120 may include information forpersons who are users of the pharmacy but are not members in thepharmacy benefit plan being provided by the PBM. For example, theseusers may obtain drugs directly from the pharmacy, through a privatelabel service offered by the pharmacy, the high-volume fulfillmentcenter, or otherwise. In general, the use of the terms “member” and“user” may be used interchangeably.

The claims data 122 includes information regarding pharmacy claimsadjudicated by the PBM under a drug benefit program provided by the PBMfor one or more plan sponsors. In general, the claims data 122 includesan identification of the client that sponsors the drug benefit programunder which the claim is made, and/or the member that purchased theprescription drug giving rise to the claim, the prescription drug thatwas filled by the pharmacy (e.g., the national drug code number, etc.),the dispensing date, generic indicator, generic product identifier (GPI)number, medication class, the cost of the prescription drug providedunder the drug benefit program, the copayment/coinsurance amount, rebateinformation, and/or member eligibility, etc. Additional information maybe included.

In some implementations, other types of claims beyond prescription drugclaims may be stored in the claims data 122. For example, medicalclaims, dental claims, wellness claims, or other types ofhealth-care-related claims for members may be stored as a portion of theclaims data 122.

In some implementations, the claims data 122 includes claims thatidentify the members with whom the claims are associated. Additionallyor alternatively, the claims data 122 may include claims that have beende-identified (that is, associated with a unique identifier but not witha particular, identifiable member).

The drug data 124 may include drug name (e.g., technical name and/orcommon name), other names by which the drug is known, activeingredients, an image of the drug (such as in pill form), etc. The drugdata 124 may include information associated with a single medication ormultiple medications.

The prescription data 126 may include information regardingprescriptions that may be issued by prescribers on behalf of users, whomay be members of the pharmacy benefit plan—for example, to be filled bya pharmacy. Examples of the prescription data 126 include user names,medication or treatment (such as lab tests), dosing information, etc.The prescriptions may include electronic prescriptions or paperprescriptions that have been scanned. In some implementations, thedosing information reflects a frequency of use (e.g., once a day, twicea day, before each meal, etc.) and a duration of use (e.g., a few days,a week, a few weeks, a month, etc.).

In some implementations, the order data 118 may be linked to associatedmember data 120, claims data 122, drug data 124, and/or prescriptiondata 126. The plan sponsor data 128 includes information regarding theplan sponsors of the PBM. Examples of the plan sponsor data 128 includecompany name, company address, contact name, contact telephone number,contact e-mail address, etc.

UI Adaptation

In various implementations, the member data 120 may also include phonecall or internet chat logs for each user and user population. Each logmay include a duration as well as a corresponding claim that the log isreferencing. The member data 120 may also include, for each user, theduration of their membership and the duration of their presence in thepopulation. The duration of membership may include a duration ofpharmacy membership, a duration of inclusion in the user population, andduration of pharmacy membership of each user population.

In various implementations, the claims data 122 may also include costdata for each member or user indicating an amount of cost the pharmacyhas incurred for each user based on customer support, shipping costs,etc. The claims data 122 may also include margin data indicating anintake of the pharmacy for each claim of each user. The intake indicatesan amount the pharmacy has benefited financially for each user. Theoutput and intake may be determined over a period of time.

In various implementations, a web portal 130 includes a personadetermination module 130-1 and a user interface adaptation module 130-2.The web portal 130 is in communication with the user device 108 and aper-user metric determination device 132 via the network 104. A user cancreate an account to manage the user prescriptions via the web portal130. In response to the user logging on to their user account, thepersona determination module 130-1 determines which persona correspondsto the user. This may prompt a per-user metric calculation (or lookup)for the user. The user interface adaptation module 130-2 receives thedetermined persona from the persona determination module 130-1 andtransforms the user interface presented to the user—for example, on theuser device 108—according to the determined persona. In this way, theuser interface presented to the user is personalized.

In various implementations, the persona determination module 130-1determines the persona that corresponds to the user based on theper-user metric of the user. The persona determination module 130-1receives the per-user metric of the user from the per-user metricdetermination device 132. The per-user metric determination device 132receives relevant information of the user from the storage device 110via the network. Based on the received relevant information, theper-user metric determination device 132 determines a retention metricof the user and a benefit metric of the user in order to determine theper-user metric. The persona determination module 130-1 then determinesthe persona that corresponds to the per-user metric.

In various implementations, the storage device 110 is a data store, adata warehouse, a relational database, a NoSQL database, or a datarepository. The storage device 110 is configured to index event data.The event data includes member data 120 and claims data 122. The datastore indexes a plurality of events, where each event corresponds to aphysical object, for example, a prescription drug, being supplied to auser or patient. For example, the prescription drug can be mailed to theuser, the user can obtain the prescription drug from a retail location,such as a pharmacy, etc. In the data store, and in the system, each useror patient can be identified using an identifier unique to theuser/patient.

The data store can further store descriptive data corresponding to eachidentifier to indicate the corresponding user. For example, thisdescriptive data can include demographic data such as age and sex,social information such as histories of smoking and drinking, and healthdata such as present and past medical conditions.

The system 100 includes an analyst device 134 including a personadetermination module 134-1 and a user interface adaptation module 134-2.The system 100 includes a support device 136 including a personadetermination module 136-1 and a user interface adaptation module 136-2.Data analysts of the pharmacy may use the analyst device 134 to runanalytics of specific users or specific user populations based onsubmitted parameters or selected relevant information. In variousimplementations, the pharmacy may analyze potential clients based onsimilar clients.

The analyst device 134 may include a homepage for an analyst to input afuture number of years, a target user or client, and additionalinformation related to the analysis. Based on the determined per-usermetric, the personas may vary according to the analyzed data. Forexample, if the per-user metric is low, a corresponding persona mayadapt the user interface to highlight potential areas of improvement,with respect to the per-user metric, for the user or client.

The support device 136 displays a user interface to a supportrepresentative according to a user or user population that the supportrepresentative is assisting. For example, the support representative maybe at a call center and receive a call from a particular user. Thesupport representative may identify the caller to the support device 136if the phone system had not yet identified the caller before connectingthe caller to the support representative. The persona determinationmodule 136-1 determines a persona of the user based on the submittedparameters and displays a user interface according to the persona. Theuser interface adaptation module 136-2 transforms the user interface. Inthis way, the support representative is displayed a customized userinterface according to the user or the user population of the usercalling for assistance, such as the ability to expedite shipping forfree or at a reduced rate.

The analyst device 134 may also be referred to as a data analyst device,where an analyst can query metrics for one or more users. The metric(s)may be displayed to the analyst, either individually or in theaggregate. In some implementations, the metric(s) may cause the userinterface of the data analyst device to be updated, such as by removinga user interface element or modifying a user interface element (such asby modifying a displayed number or changing a font characteristic).

The web portal 130 may be considered a special case of the data analystdevice, where a first user accessing the data analyst device isrestricted to only information about the first user. Further, in thecase of the web portal 130, the data analyst device may prevent displayof the metric to the first user and instead only modify the userinterface in response to the metric.

The support device 136 may also be considered a special case of the dataanalyst device, where the support representative using the data analystdevice chooses one user at a time (generally, the person whom thesupport representative is interacting with via phone, chat, etc.). Themetric for the chosen user may be displayed to the supportrepresentative. In other implementations, the metric may be used toadapt user interface elements, such as revealing or hiding buttonsrelated to expedited shipping, or modifying text describing the cost orspeed of expedited shipping. In such implementations, the metric may ormay not be shown.

Fulfillment Device

FIG. 2 illustrates the pharmacy fulfillment device 112 according to anexample implementation. The pharmacy fulfillment device 112 may be usedto process and fulfill prescriptions and prescription orders. Afterfulfillment, the fulfilled prescriptions are packed for shipping.

The pharmacy fulfillment device 112 may include devices in communicationwith the benefit manager device 102, the order processing device 114,and/or the storage device 110, directly or over the network 104.Specifically, the pharmacy fulfillment device 112 may include palletsizing and pucking device(s) 206, loading device(s) 208, inspectdevice(s) 210, unit of use device(s) 212, automated dispensing device(s)214, manual fulfillment device(s) 216, review devices 218, imagingdevice(s) 220, cap device(s) 222, accumulation devices 224, packingdevice(s) 226, literature device(s) 228, unit of use packing device(s)230, and mail manifest device(s) 232. Further, the pharmacy fulfillmentdevice 112 may include additional devices, which may communicate witheach other directly or over the network 104.

In some implementations, operations performed by one of these devices206-232 may be performed sequentially, or in parallel with theoperations of another device as may be coordinated by the orderprocessing device 114. In some implementations, the order processingdevice 114 tracks a prescription with the pharmacy based on operationsperformed by one or more of the devices 206-232.

In some implementations, the pharmacy fulfillment device 112 maytransport prescription drug containers, for example, among the devices206-232 in the high-volume fulfillment center, by use of pallets. Thepallet sizing and pucking device 206 may configure pucks in a pallet. Apallet may be a transport structure for a number of prescriptioncontainers, and may include a number of cavities. A puck may be placedin one or more than one of the cavities in a pallet by the pallet sizingand pucking device 206. The puck may include a receptacle sized andshaped to receive a prescription container. Such containers may besupported by the pucks during carriage in the pallet. Different pucksmay have differently sized and shaped receptacles to accommodatecontainers of differing sizes, as may be appropriate for differentprescriptions.

The arrangement of pucks in a pallet may be determined by the orderprocessing device 114 based on prescriptions that the order processingdevice 114 decides to launch. The arrangement logic may be implementeddirectly in the pallet sizing and pucking device 206. Once aprescription is set to be launched, a puck suitable for the appropriatesize of container for that prescription may be positioned in a pallet bya robotic arm or pickers. The pallet sizing and pucking device 206 maylaunch a pallet once pucks have been configured in the pallet.

The loading device 208 may load prescription containers into the puckson a pallet by a robotic arm, a pick and place mechanism (also referredto as pickers), etc. In various implementations, the loading device 208has robotic arms or pickers to grasp a prescription container and moveit to and from a pallet or a puck. The loading device 208 may also printa label that is appropriate for a container that is to be loaded ontothe pallet, and apply the label to the container. The pallet may belocated on a conveyor assembly during these operations (e.g., at thehigh-volume fulfillment center, etc.).

The inspect device 210 may verify that containers in a pallet arecorrectly labeled and in the correct spot on the pallet. The inspectdevice 210 may scan the label on one or more containers on the pallet.Labels of containers may be scanned or imaged in full or in part by theinspect device 210. Such imaging may occur after the container has beenlifted out of its puck by a robotic arm, picker, etc., or may beotherwise scanned or imaged while retained in the puck. In someimplementations, images and/or video captured by the inspect device 210may be stored in the storage device 110 as order data 118.

The unit of use device 212 may temporarily store, monitor, label, and/ordispense unit of use products. In general, unit of use products areprescription drug products that may be delivered to a user or memberwithout being repackaged at the pharmacy. These products may includepills in a container, pills in a blister pack, inhalers, etc.Prescription drug products dispensed by the unit of use device 212 maybe packaged individually or collectively for shipping, or may be shippedin combination with other prescription drugs dispensed by other devicesin the high-volume fulfillment center.

At least some of the operations of the devices 206-232 may be directedby the order processing device 114. For example, the manual fulfillmentdevice 216, the review device 218, the automated dispensing device 214,and/or the packing device 226, etc. may receive instructions provided bythe order processing device 114.

The automated dispensing device 214 may include one or more devices thatdispense prescription drugs or pharmaceuticals into prescriptioncontainers in accordance with one or multiple prescription orders. Ingeneral, the automated dispensing device 214 may include mechanical andelectronic components with, in some implementations, software and/orlogic to facilitate pharmaceutical dispensing that would otherwise beperformed in a manual fashion by a pharmacist and/or pharmacisttechnician. For example, the automated dispensing device 214 may includehigh-volume fillers that fill a number of prescription drug types at arapid rate and blister pack machines that dispense and pack drugs into ablister pack. Prescription drugs dispensed by the automated dispensingdevices 214 may be packaged individually or collectively for shipping,or may be shipped in combination with other prescription drugs dispensedby other devices in the high-volume fulfillment center.

The manual fulfillment device 216 controls how prescriptions aremanually fulfilled. For example, the manual fulfillment device 216 mayreceive or obtain a container and enable fulfillment of the container bya pharmacist or pharmacy technician. In some implementations, the manualfulfillment device 216 provides the filled container to another devicein the pharmacy fulfillment devices 112 to be joined with othercontainers in a prescription order for a user or member.

In general, manual fulfillment may include operations at least partiallyperformed by a pharmacist or a pharmacy technician. For example, aperson may retrieve a supply of the prescribed drug, may make anobservation, may count out a prescribed quantity of drugs and place theminto a prescription container, etc. Some portions of the manualfulfillment process may be automated by use of a machine. For example,counting of capsules, tablets, or pills may be at least partiallyautomated (such as through use of a pill counter). Prescription drugsdispensed by the manual fulfillment device 216 may be packagedindividually or collectively for shipping, or may be shipped incombination with other prescription drugs dispensed by other devices inthe high-volume fulfillment center.

The review device 218 may process prescription containers to be reviewedby a pharmacist for proper pill count, exception handling, prescriptionverification, etc. Fulfilled prescriptions may be manually reviewedand/or verified by a pharmacist, as may be required by state or locallaw. A pharmacist or other licensed pharmacy person who may dispensecertain drugs in compliance with local and/or other laws may operate thereview device 218 and visually inspect a prescription container that hasbeen filled with a prescription drug. The pharmacist may review, verify,and/or evaluate drug quantity, drug strength, and/or drug interactionconcerns, or otherwise perform pharmacist services. The pharmacist mayalso handle containers which have been flagged as an exception, such ascontainers with unreadable labels, containers for which the associatedprescription order has been canceled, containers with defects, etc. Inan example, the manual review can be performed at a manual reviewstation.

The imaging device 220 may image containers once they have been filledwith pharmaceuticals. The imaging device 220 may measure a fill heightof the pharmaceuticals in the container based on the obtained image todetermine if the container is filled to the correct height given thetype of pharmaceutical and the number of pills in the prescription.Images of the pills in the container may also be obtained to detect thesize of the pills themselves and markings thereon. The images may betransmitted to the order processing device 114 and/or stored in thestorage device 110 as part of the order data 118.

The cap device 222 may be used to cap or otherwise seal a prescriptioncontainer. In some implementations, the cap device 222 may secure aprescription container with a type of cap in accordance with a userpreference (e.g., a preference regarding child resistance, etc.), a plansponsor preference, a prescriber preference, etc. The cap device 222 mayalso etch a message into the cap, although this process may be performedby a subsequent device in the high-volume fulfillment center.

The accumulation device 224 accumulates various containers ofprescription drugs in a prescription order. The accumulation device 224may accumulate prescription containers from various devices or areas ofthe pharmacy. For example, the accumulation device 224 may accumulateprescription containers from the unit of use device 212, the automateddispensing device 214, the manual fulfillment device 216, and the reviewdevice 218. The accumulation device 224 may be used to group theprescription containers prior to shipment to the member.

The literature device 228 prints, or otherwise generates, literature toinclude with each prescription drug order. The literature may be printedon multiple sheets of substrates, such as paper, coated paper, printablepolymers, or combinations of the above substrates. The literatureprinted by the literature device 228 may include information required toaccompany the prescription drugs included in a prescription order, otherinformation related to prescription drugs in the order, financialinformation associated with the order (for example, an invoice or anaccount statement), etc.

In some implementations, the literature device 228 folds or otherwiseprepares the literature for inclusion with a prescription drug order(e.g., in a shipping container). In other implementations, theliterature device 228 prints the literature and is separate from anotherdevice that prepares the printed literature for inclusion with aprescription order.

The packing device 226 packages the prescription order in preparationfor shipping the order. The packing device 226 may box, bag, orotherwise package the fulfilled prescription order for delivery. Thepacking device 226 may further place inserts (e.g., literature or otherpapers, etc.) into the packaging received from the literature device228. For example, bulk prescription orders may be shipped in a box,while other prescription orders may be shipped in a bag, which may be awrap seal bag.

The packing device 226 may label the box or bag with an address and arecipient's name. The label may be printed and affixed to the bag orbox, be printed directly onto the bag or box, or otherwise associatedwith the bag or box. The packing device 226 may sort the box or bag formailing in an efficient manner (e.g., sort by delivery address, etc.).The packing device 226 may include ice or temperature sensitive elementsfor prescriptions that are to be kept within a temperature range duringshipping (for example, this may be necessary in order to retainefficacy). The ultimate package may then be shipped through postal mail,through a mail order delivery service that ships via ground and/or air(e.g., UPS, FEDEX, or DHL, etc.), through a delivery service, through alocker box at a shipping site (e.g., AMAZON locker or a PO Box, etc.),or otherwise.

The unit of use packing device 230 packages a unit of use prescriptionorder in preparation for shipping the order. The unit of use packingdevice 230 may include manual scanning of containers to be bagged forshipping to verify each container in the order. In an exampleimplementation, the manual scanning may be performed at a manualscanning station. The pharmacy fulfillment device 112 may also include amail manifest device 232 to print mailing labels used by the packingdevice 226 and may print shipping manifests and packing lists.

While the pharmacy fulfillment device 112 in FIG. 2 is shown to includesingle devices 206-232, multiple devices may be used. When multipledevices are present, the multiple devices may be of the same device typeor models, or may be a different device type or model. The types ofdevices 206-232 shown in FIG. 2 are example devices. In otherconfigurations of the system 100, lesser, additional, or different typesof devices may be included.

Moreover, multiple devices may share processing and/or memory resources.The devices 206-232 may be located in the same area or in differentlocations. For example, the devices 206-232 may be located in a buildingor set of adjoining buildings. The devices 206-232 may be interconnected(such as by conveyors), networked, and/or otherwise in contact with oneanother or integrated with one another (e.g., at the high-volumefulfillment center, etc.). In addition, the functionality of a devicemay be split among a number of discrete devices and/or combined withother devices.

FIG. 3 illustrates the order processing device 114 according to anexample implementation. The order processing device 114 may be used byone or more operators to generate prescription orders, make routingdecisions, make prescription order consolidation decisions, trackliterature with the system 100, and/or view order status and other orderrelated information. For example, the prescription order may becomprised of order components.

The order processing device 114 may receive instructions to fulfill anorder without operator intervention. An order component may include aprescription drug fulfilled by use of a container through the system100. The order processing device 114 may include an order verificationsubsystem 302, an order control subsystem 304, and/or an order trackingsubsystem 306. Other subsystems may also be included in the orderprocessing device 114.

The order verification subsystem 302 may communicate with the benefitmanager device 102 to verify the eligibility of the member and reviewthe formulary to determine appropriate copayment, coinsurance, anddeductible for the prescription drug and/or perform a DUR (drugutilization review). Other communications between the order verificationsubsystem 302 and the benefit manager device 102 may be performed for avariety of purposes.

The order control subsystem 304 controls various movements of thecontainers and/or pallets along with various filling functions duringtheir progression through the system 100. In some implementations, theorder control subsystem 304 may identify the prescribed drug in one ormore than one prescription orders as capable of being fulfilled by theautomated dispensing device 214. The order control subsystem 304 maydetermine which prescriptions are to be launched and may determine thata pallet of automated-fill containers is to be launched.

The order control subsystem 304 may determine that an automated-fillprescription of a specific pharmaceutical is to be launched and mayexamine a queue of orders awaiting fulfillment for other prescriptionorders, which will be filled with the same pharmaceutical. The ordercontrol subsystem 304 may then launch orders with similar automated-fillpharmaceutical needs together in a pallet to the automated dispensingdevice 214. As the devices 206-232 may be interconnected by a system ofconveyors or other container movement systems, the order controlsubsystem 304 may control various conveyors: for example, to deliver thepallet from the loading device 208 to the manual fulfillment device 216from the literature device 228, paperwork as needed to fill theprescription.

The order tracking subsystem 306 may track a prescription order duringits progress toward fulfillment. The order tracking subsystem 306 maytrack, record, and/or update order history, order status, etc. The ordertracking subsystem 306 may store data locally (for example, in a memory)or as a portion of the order data 118 stored in the storage device 110.

Per-User Determination Controller

FIG. 4 is a functional block diagram of an example per-user metricdetermination device 132. The per-user metric determination device 132receives input parameters and accesses member data 120 and claims data122 from the storage device 110 to calculate a per-user metric of auser, depending on the input parameters. While the present applicationrefers to the per-user metric of the user, a population-wide metric ofthe user population may also be determined using the per-user metricdetermination device 132.

In various implementations, for each per-user metric calculation, theper-user metric determination device 132 may identify multipleprocessing threads on each of multiple processor cores and assigncalculations of the per-user metric calculation to respective threads.To calculate the per-user metric, a retention module 400 receives inputparameters. For example, when a selected user logs into their account,the retention module 400 may receive input parameters from an account ofthe selected user. In such an implementation, the user identity may bereceived in response to the selected user logging into their account. Invarious implementations, the input parameters include the user identityand the current user population. While using an analyst device, ananalyst may input the user identity along with the user population). Theuser population may indicate demographic data or employment informationof the selected user. For example, the employees of the selected user'semployer may form the user population.

The retention module 400 calculates the amount of time the user and theuser population was retained as a patient of the organization during atime period for example, the prior year—based on the user accountinformation and user duration data maintained in the member data 120. Invarious implementations, the retention module 400 receives member data120 from the storage device 110 to obtain duration data for each memberof the user population.

The per-user metric determination device 132 also includes an expectedretention module 404. The expected retention module 404 receives theinput parameters, the member data 120, and the actual user/populationretention calculated by the retention module 400. The expected retentionmodule 404 identifies relevant demographic data in the member data 120.The relevant demographic data may include the age of the user andemployment information of the user. The expected retention module 404can compare the user's age, employment information, and actualhistorical retention to historical retention rates of users in similarcircumstances.

In various implementations, similar circumstances may be determined fromdemographic data and may include an age range, employer, etc. Thehistorical retention rates of users in similar circumstances may bedetermined from data included in the member data 120. The expectedretention module 404 calculates the expected user retention and theexpected population retention that includes the selected user based onhistorical retention rates of the selected user, the population of theselected user, and retention of users in similar circumstances. Forexample, if users in an age range of 20-25 years have an actualhistorical retention rate of 80%, the expected retention module 404 maydetermine the same expected retention rate of 80% for a 23-year-old userwhen comparing the 23-year-old user to users in the respective agerange.

An expected output module 408 receives member data 120 and claims data122 as well as the input parameters. In various implementations, theexpected output module 408 also receives historical claims data of aselected training group of approved members in the claims data 122.Members are identified as approved members if the member data (as wellas other data corresponding to the member) is allowed to be included indata processing and modeling. For example, certain members may beexcluded from data processing and modeling based on restrictions on theuse of their healthcare information.

The selected training group, for example, may include historical claimsof 500,000 approved users for a previous time period, such as the prioryear. The expected output module 408 obtains behavioral data for eachuser in the training group from the member data 120. For example, theexpected output module 408 identifies a number of times each user in thetraining group has called a support center of the pharmacy per claimused any other resource of the pharmacy (such as requesting shipment ofdrugs) over the previous period of time. The expected output module 408quantifies each use to determine a previous output of each user in thetraining group. The previous output of each user in the training groupindicates an output or cost required to serve the respective user in thetraining group. The output may include all costs incurred to serve theuser.

Similarly, the expected output module 408 determines previous outputs ofeach user of the training group during the previous period of time. Invarious implementations, an output model of the training group isincluded in the expected output module 408. The output model is atrained machine learning model that predicts future output: for example,for the next year. The training group is used to train the output modeland a test group—for example, of the same size or smaller than thetraining group—is used to verify the accuracy of the output model. Theexpected output module 408 determines an expected output of the userbased on the output model and previous user output over a future timeperiod, where the future time period is an input parameter. For example,the future time period for the web portal may be a predetermined value,such as one year. Meanwhile, an analyst may choose the future timeperiod according to their analytical task.

The per-user metric determination device 132 also includes an expectedintake module 412. The expected intake module 412 indicates an amountreceived by the pharmacy based on the claims the user has made over theprevious time period. The expected intake module 412 receives memberdata 120 and claims data 122 as well as the input parameters. In variousimplementations, the expected output module 408 and the expected intakemodule 412 are included in one module. Similar to the expected outputmodule 408, the expected intake module 412 includes an intake modelcreated according to a machine learning method. The intake model is atrained model that predicts future intake: for example, for the nextyear. In various implementations, the intake represents margin orresources received from a user for an event: for example, an amount paidby the user and/or another payer (such as a health plan) for receiving adrug. In various implementations, the intake may represent the amountthe user paid for the drug less the amount the pharmacy expended toprocure, handle, store, and ship the drug.

The output model and the intake model are trained on a regular basisinstead of being trained each time a metric is calculated. The intakemodel is created from historical claims data and extracts claims toanalyze the intake from each claim for each user over the previous timeperiod. Based on the input parameters, the expected intake module 412determines an expected intake of the user based on the intake model andprevious user intake over a future time period, where the future timeperiod is an input parameter.

The expected retention module 404, the expected output module 408, andthe expected intake module 412 may all include machine learning modelsgenerated by a data processing circuit. For example, the data processingcircuit may be included in the per-user metric determination device 132or within each of the previously listed modules. To generate therespective models, a set of users is identified. A machine learningmodel is trained based on event data of the set of identified users overa predetermined previous period or epoch—for example, the previous year.The machine learning model for the respective modules can identifyrelevant information included in the event data. For example, a modelgenerated for or by the expected output module 408 may identifyinformation regarding an output of each event to train the model basedon previous output received for the set of identified users over thepredetermined previous period.

As previously mentioned, in various implementations, the machinelearning model may be trained using parallel processing for recordsobtained from the storage device 110. The parallel processing includesassigning the analysis of the stored event data for each set ofidentified users to respective processor threads for parallel executionon processing hardware. Using parallel processing to train the modelsdescribed above is a new and efficient method to train models faster.

A per-user metric calculation module 416 receives the input parameters,the expected user retention and the expected population retention fromthe expected retention module 404, the expected output of the user fromthe expected output module 408, and the expected intake of the user fromthe expected intake module 412. The per-user metric calculation module416 calculates a per-user metric of the user based on the receivedinformation. In various implementations, the per-user metric calculationmodule 416 calculates the per-user metric according to the followingequation:

${PUM}_{k} = {A_{k}{\sum\limits_{i = 1}^{n}\; \frac{{G_{i}\left( {M_{k} - C_{k}} \right)}r^{i}}{\left( {1 + d} \right)^{i}}}}$

where PUM_(k) is the per-user metric of the user identified by aninteger identifier k, A_(k) is the starting retention age of the user, nis the number of future years to consider, G_(i) is the drop in userretention at the future year i based on the expected user retention,M_(k) is the expected intake of the user, C_(k) is the expected outputof the user, r is the expected population retention rate of the userpopulation, and d is a discount rate that a user or user population mayreceive. The per-user metric is output to the requesting device, such asthe web portal.

The per-user metric determination device 132 also includes a per-usermetric storage 420. The per-user metric storage 420 can be accessed viathe per-user metric calculation module 416. If the per-user metric ofthe user has already been calculated, the per-user metric calculationmodule 416 may retrieve the per-user metric of the user from theper-user metric storage 420. Each time a per-user metric is calculated,the per-user metric may be stored in the per-user metric storage 420.

In various implementations, the per-user metric determination device 132can determine the per-user metric of the user during a previous time.That is, the input parameters may specify a retrospective time period.The per-user metric determination device 132 will access historical dataincluded in the storage device 110 of the corresponding user. In thisway, a comparison may be made between the per-user metric of the userduring the previous time and the current per-user metric of the user.

In various implementations, the per-user metric determination device 132may be an interface circuit capable of performing all the functions ofthe per-user metric determination device 132 described above. Similarly,the per-user metric may be described as an interface metric.

Flowcharts

FIG. 5 is a flowchart of example determination of a per-user metricrequest. Control begins at 500 in response to a per-user metric request.Once the request is received, control continues to 504 where controldetermines if the per-user metric is available. For example, if theper-user metric was previously calculated and stored in an accessiblestorage, then the per-user metric is available. If the per-user metricis available, control continues to 508; otherwise, control continues to512. At 508, control determines whether the per-user metric is out ofdate. If so, control transfers to 514; otherwise, control transfers to512.

At 512, control obtains input parameters. For example, input parametersmay include a user identity, the requesting device (the web portal, thesupport device, the analyst device, etc.), a discount rate of the user,etc. Control then continues to 516 where the relevant population of theuser is identified from a time period, for example, a prior year.

Once the population relevant to the user is identified, controlcontinues to 520, where the first user is selected from the identifiedpopulation. Control proceeds to 524, where a duration of presence of theselected user in the identified population is determined. Control thencontinues to 528 to compute a percentage of the time period that theselected user was retained. That is, control calculates the amount oftime the selected user was included in the identified population duringthe time period. In this way, the amount of time the user was retainedin the identified population during the time period is calculated.

Control continues to 532 to store the completed retention of theselected user. Control proceeds to 536 to determine if another user isincluded in the identified population. If so, control proceeds to 540where the next user of the identified population is selected. Controlthen returns to 524 to determine the duration of presence of theselected user in the identified population. If, at 536, there are noadditional users to evaluate in the identified population, controlcontinues to 544, where control calculates the population retentionbased on the stored retention of users in the identified population. Inthis way, individual retention rates are calculated along with theretention rate of the entire population.

Control proceeds to 548 where control calculates the per-user metric forthe first user. FIG. 6 describes an example of per-user metriccalculation. Once calculated, the per-user metric for the first user isstored at 552. Control proceeds to 514 where the per-user metric of thefirst user is graphically displayed and control ends. In variousimplementations, the analyst device described in FIG. 1 calculates theper-user metric and displays the per-user metric along with atransformed user interface for the persona corresponding to the per-usermetric.

FIG. 6 is a flowchart of an example per-user metric calculation. Controlbegins at 600, where the stored claims data of the first user isobtained. Control continues to 604 where relevant claims for the firstuser are selected from the claims data. At 608, control calculates theexpected intake of the first user based on the selected claims. Then at612, control obtains stored member data of the first user, such as froma storage device. Control proceeds to 616, where relevant member data ofthe first user is selected. At 620, control calculates an expectedoutput of the first user based on the selected member data.

Then, at 624, control selects relevant demographic data of the firstuser from the member data. Control continues to 628, where expected userretention is calculated based on the selected demographic data andactual user retention. For example, the actual user retention may becalculated as shown in FIG. 5. At 632, control calculates the per-usermetric of the first user based on the expected intake, the expectedoutput, the expected population retention, and the expected userretention of the first user.

FIG. 7 is a flowchart of an example persona selection for a web portal.Control begins at 700, in response to a user logging on to the webportal. Once a user has logged on to the web portal, control proceeds to704 and obtains the per-user metric of the logged-on user, as describedin FIG. 6. Control then continues to 708 to determine if the per-usermetric is greater than a metric threshold. If not, control proceeds to712 to determine if a user retention is greater than a retentionthreshold. In various implementations, the user retention may be ameasure of actual user retention over the prior year. As anotherexample, the user retention may be an expected user retention for theupcoming year. If the user retention is not greater than the retentionthreshold, control continues to 716 and selects persona 1. Persona 1represents a user with a low per-user metric as well as a low retention.When the user corresponds to persona 1, the user interface will bemodified to encourage the user to achieve a higher per-user metricand/or higher user retention. The user interface may also be modified toremove higher-overhead items, such as free expedited shipping, for theuser whose retention is unlikely to be affected by such perks. Oncepersona 1 is selected at 716, control continues to 720 to transform theuser interface on the web portal to correspond to the identifiedpersona, and control ends.

Returning to 712, if the user retention is greater than the retentionthreshold, control continues to 724 and selects persona 2. Persona 2represents a user with a low per-user metric and a high user retention.When the user corresponds to persona 2, the user interface will bemodified to encourage the user to achieve a higher per-user metric andmay offer benefits based on the high user retention. Once persona 2 isselected, control continues to 720.

Returning to 708, if the per-user metric is greater than the metricthreshold, control continues to 728. At 728, control determines if theuser retention is greater than a retention threshold. If not, controlcontinues to 732 and selects persona 3. Persona 3 represents a user witha high per-user metric but a low user retention. Similar to persona 2,the user interface will be modified to encourage the user to achievehigher user retention but may provide or suggest certain benefits basedon the high per-user metric. Once persona 3 is selected, controlcontinues to 724.

Returning to 728, if the user retention is greater than the retentionthreshold, control continues to 736 and selects persona 4. Persona 4represents a user with a high per-user metric as well as a high userretention. Therefore, the user interface may offer certain benefits tothe user based on the high per-user metric and high user retention. Oncepersona 4 is selected, control continues to 724.

In various implementations, the user interface options for each personainclude two variations of the user interface that are displayed tousers. The users for each persona will be categorized into two groups. Afirst variation of a corresponding persona will be displayed to a firstgroup and a second variation of the corresponding persona will bedisplayed to a second group. In various implementations, users areassigned to each group randomly. The per-user metric of each user ofeach group will be monitored over a testing period—for example, onemonth. At the end of the testing period, the per-user metric of eachuser at the end of the testing period is compared to the per-user metricof each user at the beginning of the testing period. Then, the averageper-user metric of the first group is compared to the average per-usermetric of the second group at the beginning and end of the testingperiod. The variation that corresponds to the group that has the mostimproved averaged per-user metric by the end of the testing period isselected and used for all users of the corresponding persona.

FIG. 8 is a flowchart of an example persona selection for a supportdevice. Control begins at 800, in response to a support representativelogging onto the support device. As mentioned previously, the presentdisclosure applies to calculating a per-user metric as well as apopulation metric. For purposes of FIG. 8, the flowchart will describecalculating an average metric of the population. However, on averageFIG. 8 may instead be implemented with an individual per-user metric anda population retention value.

Once the support representative has logged on to the support device,control continues to 804 and identifies a population. For example, thesupport representative may have received a phone call and whileconducting the phone call the support representative may input theidentity of the user that is calling. The population is then identifiedbased on the user identity, such as the prescription plan to which theuser belongs. Control continues to 808 to obtain a per-user metric ofeach user in the identified population. For example, FIG. 5 describescalculating population retention, and FIG. 6 describes how to calculatea per-user metric of a particular user.

Control then continues to 812 to calculate the average per-user metricof users in the identified population and the population retention. Oncethe average is calculated, control continues to 816 to determine if theaverage per-user metric is greater than a metric threshold. If not,control continues to 822 determine if the population retention of theidentified population is greater than a retention threshold. If not,control continues to 824 and selects persona 1.

Control continues to 828, where the user interface on the support deviceis transformed according to the identified persona, and control ends.When a persona is being determined for the support device, the personasmay be different from the personas selected for the web portal. Forexample, when the identified population has a low average per-usermetric and a low retention—that is, when control selects persona one—theuser interface of the support device may display fewer benefit optionsfor the support representative to offer the identified population.Further, for every persona, the support device may display the averageper-user metric of the identified population as well as the populationretention to inform the support representative of these metrics.Additionally, when the persona indicates a higher average per-usermetric or a higher retention likelihood (such as persona 4), the userinterface of the support device may offer more benefit options for thesupport representative to offer the user.

Returning to 820, if the population retention is greater than theretention threshold, then control continues to 832 and selects persona2. Control then continues to 828. Returning to 816, if the averageper-user metric is greater than the metric threshold, then controlcontinues to 836. At 836, control determines if the population retentionis greater than the retention threshold. If so, control continues to840, selects persona 4, and continues to 828. Otherwise, controlcontinues to 844, selects persona 3, and continues to 828.

User Interface

FIG. 9A is an example user interface adaptation of a prescriptioninterface, such as a homepage or application screen for a low per-usermetric. In various implementations, a user accesses the prescriptioninterface using a user device 900. The user device 900 may be a desktopcomputer, kiosk, or mobile computing device, such as a phone or tablet.The user device 900 includes a display screen 904, which displays theprescription interface. In various implementations, when a user has alow per-user metric as well as a low user retention (personal), the userinterface of the prescription interface will provide encouragement forthe user to increase their per-user metric as well as their retentionlikelihood.

For example, the prescription interface may include a pendingprescription refills area 908 that includes a list of pending refills.The prescription interface may also include an advertisement for onlinechatting with support representatives 912. For example, as shown theadvertisement may read “Questions? Chat Online Now!” The prescriptioninterface may also include a membership advertisement 916. Themembership advertisement 916 may be a rewards program based on usercommitment, such as automatic refills of prescriptions.

In various implementations, each of the user interface adaptationmodules 130-2, 134-2, and 136-2 of FIG. 1 are configured to perform amodification of a user interface element on the user interface of therespective device based on the per-user metric. For example, as shown inFIG. 9A, the prescription interface may include a pending prescriptionrefills area 908 that includes a list of pending refills. Theprescription interface may also include an advertisement for onlinechatting with support representatives 912. For example, as shown theadvertisement may read “Questions? Chat Online Now!” The prescriptioninterface may also include a membership advertisement 916. Themembership advertisement 916 may be a rewards program based on usercommitment, such as automatic refills of prescriptions. The modificationof the instant user interface may be altering the online chatadvertisement to offer the option of calling a support representative inresponse to the per-user metric of the user. Alternatively, in variousimplementations, the advertisement user interface element may be removedfrom the user interface.

FIG. 9B is an example user interface adaptation of a prescriptioninterface for a user with a high per-user metric. The display screen 904of the user device 900 will display a different user interface for usersthat have a higher per-user metric as well as a higher user retentionlikelihood, as shown in FIG. 9B. For example, a pending prescriptionrefills area 920 may include a list of pending refills. In variousimplementations, for certain upcoming prescription refills, an expediteshipping button 924 may appear if the pending prescription refill dateis quickly approaching. Further, for the user with a higher per-usermetric, an advertisement for the user to call, rather than simply chatwith, a support representative 928 may appear on the prescriptioninterface. For example, the advertisement may read “Questions? CallNow!” Additionally, an advertisement to redeem loyalty rewards 932 mayalso appear based on the user's commitment to the pharmacy.

CONCLUSION

The foregoing description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Thebroad teachings of the disclosure can be implemented in a variety offorms. Therefore, while this disclosure includes particular examples,the true scope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure can be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Spatial and functional relationships between elements (for example,between modules) are described using various terms, including“connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitlydescribed as being “direct,” when a relationship between first andsecond elements is described in the above disclosure, that relationshipencompasses a direct relationship where no other intervening elementsare present between the first and second elements, and also an indirectrelationship where one or more intervening elements are present (eitherspatially or functionally) between the first and second elements. Asused herein, the phrase at least one of A, B, and C should be construedto mean a logical (A OR B OR C), using a non-exclusive logical OR, andshould not be construed to mean “at least one of A, at least one of B,and at least one of C.”

In the figures, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. Further, for information sentfrom element A to element B, element B may send requests for, or receiptacknowledgements of, the information to element A. The term subset doesnot necessarily require a proper subset. In other words, a first subsetof a first set may be coextensive with (equal to) the first set.

In this application, including the definitions below, the term “module”or the term “controller” may be replaced with the term “circuit.” Theterm “module” may refer to, be part of, or include processor hardware(shared, dedicated, or group) that executes code and memory hardware(shared, dedicated, or group) that stores code executed by the processorhardware.

The module may include one or more interface circuits. In some examples,the interface circuit(s) may implement wired or wireless interfaces thatconnect to a local area network (LAN) or a wireless personal areanetwork (WPAN). Examples of a LAN are Institute of Electrical andElectronics Engineers (IEEE) Standard 802.11-2016 (also known as theWIFI wireless networking standard) and IEEE Standard 802.3-2015 (alsoknown as the ETHERNET wired networking standard). Examples of a WPAN arethe BLUETOOTH wireless networking standard from the Bluetooth SpecialInterest Group and IEEE Standard 802.15.4.

The module may communicate with other modules using the interfacecircuit(s). Although the module may be depicted in the presentdisclosure as logically communicating directly with other modules, invarious implementations the module may actually communicate via acommunications system. The communications system includes physicaland/or virtual networking equipment such as hubs, switches, routers, andgateways. In some implementations, the communications system connects toor traverses a wide area network (WAN) such as the Internet. Forexample, the communications system may include multiple LANs connectedto each other over the Internet or point-to-point leased lines usingtechnologies including Multiprotocol Label Switching (MPLS) and virtualprivate networks (VPNs).

In various implementations, the functionality of the module may bedistributed among multiple modules that are connected via thecommunications system. For example, multiple modules may implement thesame functionality distributed by a load balancing system. In a furtherexample, the functionality of the module may be split between a server(also known as remote, or cloud) module and a client (or, user) module.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. Shared processor hardware encompasses asingle microprocessor that executes some or all code from multiplemodules. Group processor hardware encompasses a microprocessor that, incombination with additional microprocessors, executes some or all codefrom one or more modules. References to multiple microprocessorsencompass multiple microprocessors on discrete dies, multiplemicroprocessors on a single die, multiple cores of a singlemicroprocessor, multiple threads of a single microprocessor, or acombination of the above.

Shared memory hardware encompasses a single memory device that storessome or all code from multiple modules. Group memory hardwareencompasses a memory device that, in combination with other memorydevices, stores some or all code from one or more modules.

The term memory hardware is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium is therefore considered tangible and non-transitory. Non-limitingexamples of a non-transitory computer-readable medium are nonvolatilememory devices (such as a flash memory device, an erasable programmableread-only memory device, or a mask read-only memory device), volatilememory devices (such as a static random access memory device or adynamic random access memory device), magnetic storage media (such as ananalog or digital magnetic tape or a hard disk drive), and opticalstorage media (such as a CD, a DVD, or a Blu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks andflowchart elements described above serve as software specifications,which can be translated into the computer programs by the routine workof a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory computer-readable medium. Thecomputer programs may also include or rely on stored data. The computerprograms may encompass a basic input/output system (BIOS) that interactswith hardware of the special purpose computer, device drivers thatinteract with particular devices of the special purpose computer, one ormore operating systems, user applications, background services,background applications, etc.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language), XML (extensible markuplanguage), or JSON (JavaScript Object Notation), (ii) assembly code,(iii) object code generated from source code by a compiler, (iv) sourcecode for execution by an interpreter, (v) source code for compilationand execution by a just-in-time compiler, etc. As examples only, sourcecode may be written using syntax from languages including C, C++, C#,Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl,Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5threvision), Ada, ASP (Active Server Pages), PHP (PHP: HypertextPreprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, VisualBasic®, Lua, MATLAB, SIMULINK, and Python®.

What is claimed is:
 1. A method for dynamic adaptation of a userinterface according to data store mining, the method comprising:indexing, on a data store, event data of a plurality of events, an eventof the plurality of events corresponding to a physical object beingsupplied to a user identified by an identifier on behalf of a firstentity, the data store being configured to store descriptive data foreach of a plurality of identifiers; identifying, on a data processingcircuit, a first set of identifiers from the plurality of identifiersbased on commonality among the descriptive data stored by the data storeacross the first set of identifiers; and training, on the dataprocessing circuit, a machine learning model for the first set ofidentifiers based on event data stored by the data store for the firstset of identifiers from within a predetermined epoch, the machinelearning model being trained using parallel processing of records fromthe data store, the parallel processing including assigning analysis ofthe indexed event data of each of a subset of the first set ofidentifiers to respective processor threads for parallel execution onprocessing hardware, in response to receiving a message from a dataanalyst device, determining, on an interface circuit, an output of aselected identifier using the machine learning model from the dataprocessing circuit, the output of the selected identifier representingan amount of resources expected to be used by the selected identifierfor a second epoch subsequent to the predetermined epoch, the dataanalyst device being configured to render a data analyst user interface,the data analyst user interface including a first user interface elementand a second user interface element, the data analyst device beingfurther configured to transmit the message that identifies the selectedidentifier of the plurality of identifiers.
 2. The method of claim 1,wherein determining the interface metric for the selected identifiercomprises: determining, on the interface circuit, the interface metricfor the selected identifier based on the determined output of theselected identifier, a retention value, and a population retentionvalue, the retention value indicating a likelihood of the selectedidentifier being associated with the first entity for the second epoch,the population retention value indicating a likelihood of a populationof identifiers encompassing the selected identifier being associatedwith the first entity for the second epoch.
 3. The method of claim 1,further comprising: rendering, on the data analyst device, the dataanalyst user interface; and transmitting, on the data analyst device,the message that identifies the selected identifier; in response to theinterface metric from the interface circuit, selectively performing onthe data analyst device at least one of modification and removal of thesecond user interface element.
 4. The method of claim 3 wherein thesecond user interface element indicates a shipping option of a drug. 5.The method of claim 1, wherein the amount of resources expected to beused by the selected identifier includes at least one of: (i) anexpected number of calls received from the selected identifier, (ii) anexpected number of drug orders mailed to the selected identifier; and(iii) an expected measure of drugs dispensed to the selected identifier.6. The method of claim 1, further comprising: determining, on theinterface circuit, an intake of the selected identifier using a machinelearning model from the data processing circuit, the intake of theselected identifier representing an amount of resources expected to bereceived by the first entity from the selected identifier for the secondepoch; and determining, on the interface circuit, the interface metricfor the selected identifier based on the determined output of theselected identifier and the determined intake of the selectedidentifier.
 7. The method of claim 6, wherein the amount of resourcesexpected to be received from the selected identifier is a difference,for each event of the selected identifier, between an amount receivedfor the event and an amount expended for the event.
 8. The method ofclaim 6, further comprising: identifying, on the interface circuit, apopulation of identifiers encompassing the selected identifier;determining, on the interface circuit, a previous retention value of theselected identifier based on a duration of presence of the selectedidentifier within the identified population during the predeterminedepoch; and determining, on the interface circuit, a previous populationretention value of the identified population based on a duration ofpresence of the identified population as a client of the first entityduring the predetermined epoch.
 9. The method of claim 1, furthercomprising: determining, on the interface circuit, an interface metricfor the selected identifier based on the determined output of theselected identifier; and transmitting, on the interface circuit, theinterface metric to the data analyst device, the data analyst devicebeing configured to, in response to the interface metric from theinterface circuit, selectively perform at least one of modification andremoval of the second user interface element.
 10. A non-transitorycomputer-readable medium storing processor-executable instructions, theinstructions comprising: indexing, on a data store, event data of aplurality of events, an event of the plurality of events correspondingto a physical object being supplied to a user identified by anidentifier on behalf of a first entity, the data store being configuredto store descriptive data for each of a plurality of identifiers;identifying, on a data processing circuit, a first set of identifiersfrom the plurality of identifiers based on commonality among thedescriptive data stored by the data store across the first set ofidentifiers; and training, on the data processing circuit, a machinelearning model for the first set of identifiers based on event datastored by the data store for the first set of identifiers from within apredetermined epoch, the machine learning model being trained usingparallel processing of records from the data store, the parallelprocessing including assigning analysis of the indexed event data ofeach of a subset of the first set of identifiers to respective processorthreads for parallel execution on processing hardware, in response toreceiving a message from a data analyst device, determining, on aninterface circuit, an output of a selected identifier using the machinelearning model from the data processing circuit, the output of theselected identifier representing an amount of resources expected to beused by the selected identifier for a second epoch subsequent to thepredetermined epoch, the data analyst device being configured to rendera data analyst user interface, the data analyst user interface includinga first user interface element and a second user interface element, thedata analyst device being further configured to transmit the messagethat identifies the selected identifier of the plurality of identifiers.11. The non-transitory computer-readable medium of claim 10, whereindetermining the interface metric for the selected identifier comprises:determining, on the interface circuit, the interface metric for theselected identifier based on the determined output of the selectedidentifier, a retention value, and a population retention value, theretention value indicating a likelihood of the selected identifier beingassociated with the first entity for the second epoch, the populationretention value indicating a likelihood of a population of identifiersencompassing the selected identifier being associated with the firstentity for the second epoch.
 12. The non-transitory computer-readablemedium of claim 10, the instructions further comprising: rendering, onthe data analyst device, the data analyst user interface; andtransmitting, on the data analyst device, the message that identifiesthe selected identifier; in response to the interface metric from theinterface circuit, selectively performing on the data analyst device atleast one of modification and removal of the second user interfaceelement.
 13. The non-transitory computer-readable medium of claim 12,wherein the second user interface element indicates a shipping option ofa drug.
 14. The non-transitory computer-readable medium of claim 12,wherein the amount of resources expected to be used by the selectedidentifier includes at least one of: (i) an expected number of callsreceived from the selected identifier, (ii) an expected number of drugorders mailed to the selected identifier; and (iii) an expected measureof drugs dispensed to the selected identifier.
 15. The non-transitorycomputer-readable medium of claim 10, the instructions furthercomprising: determining, on the interface circuit, an intake of theselected identifier using a machine learning model from the dataprocessing circuit, the intake of the selected identifier representingan amount of resources expected to be received by the first entity fromthe selected identifier for the second epoch; and determining, on theinterface circuit, the interface metric for the selected identifierbased on the determined output of the selected identifier and thedetermined intake of the selected identifier.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the amount of resourcesexpected to be received from the selected identifier is a difference,for each event of the selected identifier, between an amount receivedfor the event and an amount expended for the event.
 17. Thenon-transitory computer-readable medium of claim 15, the instructionsfurther comprising: identifying, on the interface circuit, a populationof identifiers encompassing the selected identifier; determining, on theinterface circuit, a previous retention value of the selected identifierbased on a duration of presence of the selected identifier within theidentified population during the predetermined epoch; and determining,on the interface circuit, a previous population retention value of theidentified population based on a duration of presence of the identifiedpopulation as a client of the first entity during the predeterminedepoch.
 18. The non-transitory computer-readable medium of claim 10, theinstructions further comprising: determining, on the interface circuit,an interface metric for the selected identifier based on the determinedoutput of the selected identifier; and transmitting, on the interfacecircuit, the interface metric to the data analyst device, the dataanalyst device being configured to, in response to the interface metricfrom the interface circuit, selectively perform at least one ofmodification and removal of the second user interface element.